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arXiv · 1708.06235

Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning

Joao Vieira·Erik Leitinger·Muris Sarajlic·Xuhong Li·Fredrik Tufvesson
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Abstract

This paper provides an initial investigation on the application of convolutional neural networks (CNNs) for fingerprint-based positioning using measured massive MIMO channels. When represented in appropriate domains, massive MIMO channels have a sparse structure which can be efficiently learned by CNNs for positioning purposes. We evaluate the positioning accuracy of state-of-the-art CNNs with channel fingerprints generated from a channel model with a rich clustered structure: the COST 2100 channel model. We find that moderately deep CNNs can achieve fractional-wavelength positioning accuracies, provided that an enough representative data set is available for training.

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Joao Vieira, Erik Leitinger, Muris Sarajlic, Xuhong Li, Fredrik Tufvesson. 2017-08-21. Deep Convolutional Neural Networks for Massive MIMO Fingerprint-Based Positioning. https://arxiv.org/abs/1708.06235

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